Method and apparatus for decompression of SAR images

ABSTRACT

A computer system for decompressing synthetic aperture radar (SAR) images includes a database for storing SAR images to be decompressed, and a processor for decompressing a SAR image from the database. The decompressing includes receiving the SAR image, performing a dynamic range compression on the SAR image, and quantizing the compressed SAR image. The quantized compressed SAR image is then decompressed by applying an anistropic diffusion algorithm thereto.

FIELD OF THE INVENTION

The present invention relates to the field of image processing, and moreparticularly, to processing synthetic aperture radar (SAR) images.

BACKGROUND OF THE INVENTION

The resolution of SAR data is not comparable to the resolution ofelectro-optical (EO) data. EO sensors include photographic and otheroptical imaging devices, such as light detection and ranging (LIDAR)collectors. EO sensors are passive in that they capture the reflectivityof light from scenes to provide photographic images thereof. However, EOsensors are limited by time-of-day and atmospheric conditions.

A synthetic aperture radar (SAR) is advantageous in that images can beacquired day or night, as well as in inclement weather. A SAR is activein that it records back-scattered radiation from radio frequency (RF)signals to generate SAR images. Each resolution cell of the SARgenerally has many scatterers. The phases of the return signals fromthese scatterers are randomly distributed, and the resultinginterference causes speckle.

Speckle gives a grainy appearance in the detected image that is finallyviewed, and hence a lower resolution when compared to an EO image.Speckle imposes a significant limitation on the accuracy of themeasurements that can be made. For instance, mensuration is ofteninclusive in SAR data. Side-lobe interference also creates a noisy lookto the SAR data. In addition, hardware malfunctions or radiointerference can decrease the fidelity of the SAR data.

SAR data is currently being treated with some form of apodization inwhich the main and side lobes are removed. However, apodization makesSAR data look binary. This also results in the detected image having agrainy appearance. SAR data is also being treated with low pass filters,such as Taylor weighting. However, the scatterers can become blurredtogether resulting in a reduced resolution. As a result of the currentapproaches used to treat SAR data, certain analysis applications can beinclusive, including registration, road detection, change detection,elevation extraction and mensuration.

For SAR images that contain speckle, an enhancement goal is to removethe speckle without destroying important image features. The brightnessof a pixel is determined not only by properties of the scatterers in theresolution cell, but also by the phase relationships between the returnsfrom these scatterers. In certain applications, however, the removal ofspeckle may be counterproductive. An example in which specklepreservation is important is where detection of features is of the samescale as the speckle patterns. A known technique for despeckling SARdata as well as resolution enhancement is the application of anisotropicdiffusion algorithms.

One approach for despeckling SAR data is disclosed in the article titled“Speckle Reducing Anisotropic Diffusion” by Yu et al. A partialdifferential equation (PDE) approach is used for speckle removal. Inparticular, an image scale space is generated, which is a set offiltered images that vary from fine to coarse. Another approach isdisclosed in the article titled “Anisotropic Diffusion Despeckling ForHigh Resolution SAR Images” by Xi et al. A non-linear diffusionfiltering algorithm based on a discretization scheme, i.e., an additiveoperator splitting (AOS) scheme, is applied in the discrete image data.While both of these approaches result in improving the resolution of theSAR data by reducing noise and preserving edges, there is still a demandto make SAR data look more like high resolution EO data.

SUMMARY OF THE INVENTION

In view of the foregoing background, it is therefore an object of thepresent invention to improve the resolution of SAR data to look morelike EO data.

This and other objects, features, and advantages in accordance with thepresent invention are provided by a computer-implemented method forprocessing synthetic aperture radar (SAR) images comprising determiningnoise in a SAR image to be processed, selecting a noise threshold forthe SAR image based on the determined noise, and mathematicallyadjusting an anisotropic diffusion algorithm based on the selected noisethreshold. The adjusted anisotropic diffusion algorithm is applied tothe SAR image.

The noise may be determined based on statistical analysis of thegradient values of the SAR image. The statistical analysis may be basedon a standard deviation of the gradient values plus a constant, forexample. Alternatively, the noise may be determined based on a Fourierwindowing scheme or a wavelet decomposition.

The anisotropic diffusion algorithm may be based on a heat equationcomprising a non-constant term. Mathematically adjusting the anisotropicdiffusion algorithm may comprise adjusting the non-constant term basedon the selected noise threshold. By adjusting the non-constant term,this advantageously allows the heat equation to be tailored to the SARdata being processed. As a result, a class of functions can be createdfor obtaining the desired results, wherein each function corresponds tospecific SAR data being processed. Disparate SAR data sets can be betterprocessed for improving the resolution of the viewed SAR image.

Another aspect of the invention is directed to a complex anisotropicdiffusion algorithm. The equations for the above described anisotropicdiffusion algorithm have been re-derived for complex data so that theprocess is now non-linear. In terms of complex data, the real andimaginary components of a SAR data set are processed at a same time.Since the real and imaginary components of the SAR data set are beingtreated as a complex object, the complex anisotropic diffusion algorithmis able to pull out scene content from extremely noisy data, which inturn improves the resolution of the viewed SAR image.

A computer-implemented method for processing complex SAR imagescomprises receiving a complex SAR data set for a SAR image comprising aplurality of pixels, and applying the complex anisotropic diffusionalgorithm to the complex SAR data set. The complex SAR data setcomprises a real and an imaginary part for each pixel. If the complexSAR dataset is received in frequency space, the frequency space isconverted to image space. The frequency space corresponds to phase andpower for each pixel, and image space corresponds to phase and amplitudefor each pixel.

The complex anisotropic diffusion algorithm may also be used ininterferometric processing of SAR data, particularly for subsidencemeasurements in urban areas, for example. Subsidence is a terraindisplacement in which the elevation of the earth's surface is decreasingrelative to sea level.

A computer-implemented method for processing interferometric SAR imagescomprises receiving first and second complex SAR data sets of a samescene, with the second complex SAR data set being offset in phase withrespect to the first complex SAR data set. Each complex SAR data set maycomprise a plurality of pixels. An interferogram is formed based on thefirst and second complex SAR data sets for providing a phase differencetherebetween. The complex anisotropic diffusion algorithm is applied tothe interferogram, wherein the interferogram comprises a real and animaginary part for each pixel. A shock filter is applied to theinterferogram.

The complex anisotropic diffusion algorithm locally mitigates noisewhile at the same time preserving scene discontinuities in theinterferogram. The shock filter is used for image deblurring. Thenon-linear smoothing via the complex anisotropic algorithm and theboundary enhancement via the shock filter increases the accuracy andquality of the phase difference measurement. Since subsidence ismeasured using persistent objects in the scene (i.e., buildings) asreference points, improving boundary quality of the persistent objectsimproves the subsidence measurement.

The method may further comprise performing a two-dimensional variationalphase unwrapping on the interferogram after application of the shockfilter. The phase difference between the two registered SAR images isrelated to a desired physical quantity of interest, such as surfacetopography. The phase difference can be registered only modulo 2π, andcurrent interferometric techniques mainly recover the absolute phase(the unwrapped phase) from the registered one (wrapped phase) usingdiscrete values which has a tendency to smooth the data.

The variational phase unwrapping algorithm in accordance with thepresent invention may be based on a cost function for controlling thesmoothing. Instead of providing a global smoothing based on theproperties of the data, the variational phase unwrapping algorithmleaves edges intact and selectively smoothes the area adjacent theedges. As a result, interferometric processing of SAR data based on thecomplex anisotropic diffusion algorithm, the shock filter and thevariational phase unwrapping collectively improve boundary quality whichin turn improves the subsidence measurement.

The anisotropic diffusion algorithm may also be used in compressing anddecompressing SAR images. An advantage of applying the anisotropicdiffusion algorithm is that the size of the SAR image after compressionresults in a smaller size file, regardless of the compression schemeused. In terms of decompressing a SAR image, by dynamically compressingthe SAR data, quantizing that data, and then decompressing with theanisotropic diffusion algorithm a smaller size file is also achieved.The greater the dynamic range the better the compression ratio. As aresult, storage and transmission of the compressed and decompressed SARimages based on the anisotropic diffusion algorithm occupies less spaceand bandwidth.

The anisotropic diffusion algorithm may also be used in elevationextraction and registration for SAR images. A computer-implementedmethod for registering SAR images comprises selecting first and secondSAR images to be registered, individually processing the selected firstand second SAR images with an anisotropic diffusion algorithm, andregistering the first and second SAR images after the processing. Themethod may further comprise applying a shock filter to the respectivefirst and second processed SAR images before the registering. Thisscheme provides higher accuracy for SAR image registration, which inturn allows elevation data to be better extracted based on theregistered SAR images.

Yet another application of the anisotropic diffusion algorithm and ashock filter is with respect to vector and road extraction for materialclassification. A computer-implemented method for vector extraction inSAR images comprises selecting a SAR image for vector extraction,processing the selected SAR image with an anisotropic diffusionalgorithm, and extracting vector data based on the processed SAR image.The shock filter may be applied to the processed SAR image before theextracting. Road image data may then be extracted based on the extractedvector data. The data is thus delineated using a coherent scheme of theanisotropic diffusion algorithm. This scheme provides higher accuracyfor road extraction.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of collecting and processing SARimages in accordance with the present invention.

FIGS. 2 and 3 are respective distribution plots of pixel intensities andgradient values for a SAR data set in accordance with the presentinvention.

FIG. 4 is an image of the SAR data set corresponding to the plots shownin FIGS. 2 and 3.

FIG. 5 is an image of the gradients corresponding to the plots shown inFIGS. 2 and 3.

FIG. 6 is an image of gradients enhanced with a weighting scheme inaccordance with the prior art.

FIG. 7 is the same image of gradients shown in FIG. 6 enhanced with ananisotropic diffusion algorithm in accordance with the presentinvention.

FIG. 8 is an original image before filtering in accordance with thepresent invention.

FIGS. 9, 10 and 11 are images corresponding to the original image shownin FIG. 8 after filtering in accordance with the prior art.

FIG. 12 is an image corresponding to the original image shown in FIG. 8after filtering with an anisotropic diffusion algorithm in accordancewith the present invention.

FIG. 13 is an original image before filtering in accordance with thepresent invention.

FIG. 14 is an image corresponding to the original image shown in FIG. 13after filtering with a complex anisotropic diffusion algorithm inaccordance with the present invention.

FIG. 15 is a flow chart illustrating non-linear processing ofinterferometric SAR data for subsidence measurements in accordance withthe present invention.

FIG. 16 is an original close vector multi-spectral image beforeapplication of a shock filter in accordance with the present invention.

FIG. 17 is an image corresponding to the original image shown in FIG. 16after application of the shock filter in accordance with the presentinvention.

FIGS. 18A and 18B are two-dimensional and three-dimensional images of anoriginal scene before interferometric processing in accordance with thepresent invention.

FIGS. 19A-22B are two-dimensional and three-dimensional imagescorresponding to the original scene shown in FIGS. 18A and 18Billustrating various stages of application of interferometric SARprocessing in accordance with the present invention.

FIG. 23 is a top down two-dimensional image of an original scene beforeinterferometric processing in accordance with the present invention.

FIGS. 24-27 are top down two-dimensional images corresponding to theoriginal scene shown in FIG. 23 illustrating various stages ofapplication of interferometric SAR processing in accordance with thepresent invention.

FIG. 28 is an original image before filtering in accordance with thepresent invention.

FIG. 29 is an image corresponding to the original image shown in FIG. 28after application of a Gaussian filter in accordance with the prior art.

FIG. 30 is an image corresponding to the original image shown in FIG. 28after multiple iterations of filtering with a complex anisotropicdiffusion algorithm in accordance with the present invention.

FIGS. 31-33 are images illustrating compression of SAR data inaccordance with the present invention.

FIGS. 34-36 are images illustrating decompression of SAR data inaccordance with the present invention.

FIGS. 37-44 are images and plots illustrating registration of SAR imagesin accordance with the present invention.

FIGS. 45-48 are images illustrating road extraction in accordance withthe present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention will now be described more fully hereinafter withreference to the accompanying drawings, in which preferred embodimentsof the invention are shown. This invention may, however, be embodied inmany different forms and should not be construed as limited to theembodiments set forth herein. Rather, these embodiments are provided sothat this disclosure will be thorough and complete, and will fullyconvey the scope of the invention to those skilled in the art. Likenumbers refer to like elements throughout.

Referring initially to FIG. 1, a synthetic aperture radar (SAR) 50collects SAR data and a computer-implemented system 60 processes the SARdata. The SAR 50 is carried by an airborne platform 52, such as anaircraft, over an area of interest. The airborne platform 52 could alsobe space-based.

The illustrated area of interest is an urban area, such as a city, thatincludes a number of buildings 54. Also included in the urban area arerelatively small features such as trees 56 and roads 58, for example, ascompared to the buildings 54. Alternatively, the area of interest couldbe a rural area, with very few if not any buildings 54.

Those of skill in the art will appreciate that a SAR image is firstreceived as a complex value before being converted to a real value forviewing. The SAR image is initially received in k space that includesphase and power. By taking the inverse Fourier transform of the returneddata in k space, an image space is generated. The image space includesphase and amplitude. Since the image space includes phase and amplitudeinformation, it suffers from speckle. To view the SAR image, the phaseis removed from the image space by taking the magnitude of the data inimage space. This produces a detected or real image for viewing.

Once the SAR images are collected, they may be stored on a storagemedium 70, such as a magnetic disk, for example, for transfer to acomputer 62. Within the computer 62, the SAR images may be stored aspart of a database of SAR images to be processed. Of course, othersuitable methods for transferring SAR data may also be used, as readilyappreciated by those skilled in the art. The collected SAR images may becomplex or real valued.

A display 64 is connected to the computer 62 for viewing the SAR imagesafter processing. Input devices such as a keyboard 66 and mouse 68 arealso connected to the computer 62. In accordance with the presentinvention, the computer 62 includes a processor 68 for processing theSAR images.

One aspect for improving the resolution of SAR data to look more like EOdata is based upon modifying a heat equation, which is a second orderlinear partial differential equation. The heat equation is as follows:

$\begin{matrix}\begin{matrix}{{\frac{\partial{u\left( {x,t} \right)}}{\partial t} = {\nabla^{2}{u\left( {x,t} \right)}}},{c \in {\mspace{20mu}{or}}}} \\{{{div}\left( {c\;{\nabla{u\left( {x,t} \right)}}} \right)} = {{\nabla{\cdot c}}\;{\nabla{u\left( {x,t} \right)}}}}\end{matrix} & (1)\end{matrix}$

Under certain conditions, a fundamental solution of the heat equation isthe Gaussian density function. The heat equation can also be written asfollows:

$\begin{matrix}\begin{matrix}{{\frac{\partial{u\left( {x,t} \right)}}{\partial t} = {{div}\;\left( {{c\left( {x,y,t} \right)}{\nabla{u\left( {x,y,t} \right)}}} \right)}},\;{c \in}} \\{= {{{c\left( {x,y,t} \right)}{\nabla^{2}{u\left( {x,y,t} \right)}}} + {{\nabla{c\left( {x,y,t} \right)}} \cdot {\nabla\left( {x,y,t} \right)}}}}\end{matrix} & (2)\end{matrix}$

The variable c is known as a convection function, and t corresponds totime, and x and y forms a complex number based upon the collected SARdata. In accordance with the present invention, the variable c is not aconstant value. This advantageously allows the heat equation to betailored to the SAR data being processed since c is not a constantvalue.

As a result, a class of functions can be created for obtaining thedesired results, wherein each function corresponds to specific SAR databeing processed. Within an urban area, the scatterers in one SAR dataset may be different from the scatterers in another SAR data set so thatthe respective SAR data sets are disparate.

Since the SAR data sets are not similar, application of a same function(i.e., an anisotropic diffusion algorithm) results in improving theresolution of the SAR data set more closely matched to the function. Forthe other SAR data set that is not closely matched to the function, theresolution thereof will not be as good as if a more closely matchedfunction was used. The same may be said about data sets from ruralareas.

By changing the convection function c to better match a particular SARdata set, then the corresponding real value image for viewing will havea better resolution than if c was a constant value. When the convectionfunction c is a constant value, the disparate SAR data sets are treatedequally. A non-constant c allows the anisotropic diffusion algorithm tosimultaneously blur and sharpen a SAR data set. By mathematicallyadjusting the heat equation via the convection function c, disparate SARdata sets can be better processed.

To mathematically adjust the heat equation, noise in the SAR data setneeds to be determined. One approach for determining noise is based ongathering statistics on the gradient values in each SAR data set to beprocessed. The statistics can be gathered on the actual SAR data setitself, or they may be predetermined based on similar SAR data sets thathave already been processed. Other approaches for determining noiseinclude a Fourier windowing scheme or a wavelet decomposition, asreadily understood by those skilled in the art.

Reference is directed to FIGS. 2-4 to illustrate calculation of thenoise in a SAR data set based on statistical analysis, which in turn isused to mathematically adjust the convection function c within theanisotropic diffusion algorithm to be applied to the SAR data set. Adistribution of the pixel intensities for the SAR data set is providedin FIG. 2, whereas distribution of the gradient values for the pixels isprovided in FIG. 3. The distribution of gradient values is the number ofgradient values at a same value. For instance, the spike 100 that peaksat 12×10⁴ means that there are 120,000 gradients that are at the samevalue.

Gathering statistics on the noise may be based on a standard deviationof the gradient values. Once the standard deviation is determined, apredetermined constant may be added to a multiple of the standarddeviation to obtain the noise threshold k. Once the noise threshold khas been determined for the SAR data set, the corresponding anisotropicdiffusion algorithm is applied to smooth the values to the right of kwhile not smoothing the values to the left of k. By not smoothing thevalues to the left of the k, the edges in the scene are preserved.

The standard deviation for the illustrated distribution of gradientvalues in FIG. 3 is indicated by the line corresponding to reference102. The threshold k is set as two times the standard deviation plus aconstant. Variations of this approach as well as other statisticalapproaches may be used to select the desired threshold k, as readilyappreciated by those skilled in the art.

The threshold k is known as the k value. High gradient values correspondto bright scatterers, which are to remain unchanged. Therefore, the kvalue is set without blurring the bright scatterers. In contrast, thegradient values that look similar are to be smoothed. By adjusting thethreshold k, different classes of functions can be used to create thedesired results specific to the SAR data set being processed. Anadvantage of adjusting the convection function c via the noise thresholdk is that the end user does not have to select among multipleanisotropic diffusion algorithms the one that is better suited forprocessing the SAR data set. Instead, this selection is doneautonomously once the noise threshold k has been selected.

The distribution of pixels intensities and gradient values in FIGS. 2and 3 correspond to the image shown in FIG. 4, and to the image of thegradients shown in FIG. 5. The two images are very similar. Each imageincludes a dB scale 110 representing the amount of brightness forviewing the image.

For the image of gradients displayed in FIG. 6, a conventional weightingscheme, such as Taylor weighting for example, has been applied duringprocessing of the SAR data set. The illustrated captions point out thatthe building edges are not clear, nor can the trees and shadows beeasily determined.

In FIG. 7, the noise threshold k for the SAR data set has been set basedupon a statistical analysis of distribution of the gradient values, asdiscussed above. The tailored filtering, which may also be referred toas a smart filter, better matches the SAR data being processed byadjusting where the smoothing is to be performed. As a result, theillustrated captions point out that the building edges are clearer, andthe trees and shadows are clearer which is advantageous for bettermensuration.

Comparisons of the same scene using different filtering schemes will nowbe discussed in reference to FIGS. 8-12. The various scatterers ofinterest are circled in each figure. The original scene before anyfiltering is shown in FIG. 8. In FIG. 9, filtering of the original sceneis based on a grid window of 9. Each group of 3×3 pixels is averaged,and this is repeated for all the pixels in the SAR data set. A standardGaussian filter has been applied to the original scene as shown in FIG.10, and a standard anisotropic diffusion algorithm has been applied tothe original scene as shown in FIG. 11.

To better remove the noise around the scatterers in the original scene,while leaving the components of the scatterers intact, the noise in theSAR data set is first determined. Based upon the selected noisethreshold k, the anisotropic diffusion algorithm is adjusted accordinglyto provide a higher resolution image, as shown in FIG. 12.

Even though an anisotropic diffusion algorithm has been applied to thescene in FIG. 11, the scatterers still have what is known asmulti-bounce around them. With multi-bounce, the waves hit thescatterers and interface with the ground, and as a result, bounce allaround the scatterers. The multi-bounce looks like noise, but in somesituations, can disclose helpful information about the scatterers. InFIG. 12, the convection function c has been selected so that themulti-bounce has been removed.

The advantage of selectively controlling the convection function c basedon a statistical analysis of the distribution of gradient values for theSAR data set being processed provides increased resolution. Intra-regionsmoothing and edge preservation is provided for images corrupted byadditive noise. In cases where the SAR data sets contains speckle, theanisotropic diffusion algorithm with the adjustable convection functionc produces edge-sensitive speckle reduction.

The selectively controlled convection function c can advantageously beapplied on raw complex data (i.e., real and imaginary components) anddetected images (i.e., only real components) using hardware and/orsoftware to improve the overall fidelity of the SAR data set. This canalso be done autonomously based upon selection of the noise thresholdfor the SAR data sets being processed. High resolution EO like scenescan thus be created from SAR data sets. By simultaneously removing noiseand smoothing similar data areas in the SAR data set, high frequencydata is preserved. Consequently, information texture and linearstructures are preserved which provides a more accurate assessmentbetween EO and SAR data.

Another aspect of the invention is directed to a complex anisotropicdiffusion algorithm. The equations for the anisotropic diffusionalgorithm as discussed above have been re-derived for complex data sothat the process is now non-linear. In terms of complex data, the realand imaginary components of a SAR data set are processed at a same time.

In contrast, even though anisotropic diffusion algorithms have beenapplied to complex SAR data, it has been done so in a linear fashion.This means that the real and imaginary components of the complex SARdata are processed separately, and then the results are combinedtogether.

The re-derived equations for the complex anisotropic diffusion algorithmare as follows:

$\begin{matrix}\begin{matrix}{{{div}\left( {{g\left( {\nabla I} \right)}{\nabla I}} \right)} = {{\frac{\partial}{\partial x}\left\{ {\left( \frac{\partial a}{\partial x} \right)\frac{K^{2}}{\begin{matrix}{K^{2} + \left( \frac{\partial a}{\partial x} \right)^{2} + \left( \frac{\partial b}{\partial x} \right)^{2} +} \\{\left( \frac{\partial a}{\partial y} \right)^{2} + \left( \frac{\partial b}{\partial y} \right)^{2}}\end{matrix}}} \right\}} +}} \\{z\frac{\partial}{\partial x}\left\{ {\left( \frac{\partial b}{\partial x} \right)\frac{K^{2}}{\begin{matrix}{K^{2} + \left( \frac{\partial a}{\partial x} \right)^{2} + \left( \frac{\partial b}{\partial x} \right)^{2} +} \\{\left( \frac{\partial a}{\partial y} \right)^{2} + \left( \frac{\partial b}{\partial y} \right)^{2}}\end{matrix}}} \right\}} \\{= {{\frac{\partial^{2}a}{\partial x^{2}}\left( \frac{K^{2}}{\begin{matrix}{K^{2} + \left( \frac{\partial a}{\partial x} \right)^{2} + \left( \frac{\partial b}{\partial x} \right)^{2} +} \\{\left( \frac{\partial a}{\partial y} \right)^{2} + \left( \frac{\partial b}{\partial y} \right)^{2}}\end{matrix}} \right)} +}} \\{{\frac{\partial a}{\partial x}\left( \frac{- {K^{2}\begin{pmatrix}{{2\left( \frac{\partial a}{\partial x} \right)\left( \frac{\partial^{2}a}{\partial x^{2}} \right)} + {2\left( \frac{\partial b}{\partial x} \right)\left( \frac{\partial^{2}b}{\partial x^{2}} \right)} +} \\{{2\left( \frac{\partial a}{\partial y} \right)\left( \frac{\partial^{2}a}{{\partial x}{\partial y}} \right)} + {2\left( \frac{\partial b}{\partial y} \right)\left( \frac{\partial^{2}b}{{\partial x}{\partial y}} \right)}}\end{pmatrix}}}{\left( {K^{2} + \left( \frac{\partial a}{\partial x} \right)^{2} + \left( \frac{\partial b}{\partial x} \right)^{2} + \left( \frac{\partial a}{\partial y} \right)^{2} + \left( \frac{\partial b}{\partial y} \right)^{2}} \right)^{2}} \right)} +} \\{z\begin{pmatrix}{{\frac{\partial^{2}b}{\partial x^{2}}\left( \frac{K^{2}}{K^{2} + \left( \frac{\partial a}{\partial x} \right)^{2} + \left( \frac{\partial b}{\partial x} \right)^{2} + \left( \frac{\partial a}{\partial y} \right)^{2} + \left( \frac{\partial b}{\partial y} \right)^{2}} \right)} +} \\{\frac{\partial b}{\partial x}\left( \frac{- {K^{2}\begin{pmatrix}{{2\left( \frac{\partial a}{\partial x} \right)\left( \frac{\partial^{2}a}{\partial x^{2}} \right)} + {2\left( \frac{\partial b}{\partial x} \right)\left( \frac{\partial^{2}b}{\partial x^{2}} \right)} +} \\{{2\left( \frac{\partial a}{\partial y} \right)\left( \frac{\partial^{2}a}{{\partial x}{\partial y}} \right)} + {2\left( \frac{\partial b}{\partial y} \right)\left( \frac{\partial^{2}b}{{\partial x}{\partial y}} \right)}}\end{pmatrix}}}{\left( {K^{2} + \left( \frac{\partial a}{\partial x} \right)^{2} + \left( \frac{\partial b}{\partial x} \right)^{2} + \left( \frac{\partial a}{\partial y} \right)^{2} + \left( \frac{\partial b}{\partial y} \right)^{2}} \right)^{2}} \right)}\end{pmatrix}}\end{matrix} & (3)\end{matrix}$

Since the real and imaginary components of the SAR data set are beingtreated as a complex object, the complex anisotropic diffusion algorithmis able to pull out scene content from extremely noisy data, which inturn improves the resolution of the viewed image.

As a comparison, reference is directed to the original image shown inFIG. 13. Complex anisotropic diffusion is applied to the SAR data setcorresponding to the original SAR image in FIG. 13 to provide thediffused image shown in FIG. 14. The boundaries and features arenoticeably sharper in the diffused image.

In addition to the complex anisotropic diffusion algorithm being appliedto single image SAR data sets, it may also be applied to interferometricprocessing. In particular, interferometric processing for subsidencemeasurement for urban scene is particularly beneficial when using thecomplex anisotropic diffusion algorithm.

Interferometric processing of SAR data will now be discussed in greaterdetail. Referring to the flowchart in FIG. 15, interferometricprocessing of SAR data for subsidence measurements in urban areas willbe discussed as an illustrated example. Subsidence is a terraindisplacement in which the elevation of the earth's surface is decreasingrelative to sea level.

SAR images are received at a trim phase history Block 120. For purposesof discussion, two SAR images are being compared. The two SAR images areof the same scene but the images are slightly offset from one another,as readily appreciated by those skilled in the art. If the SAR imagesare received as raw data, they are converted from frequency space toimage space. Frequency space corresponds to phase and power, whereasimage space corresponds to phase and amplitude. The phase and amplitudefor each pixel in the SAR image provide the real and imaginarycomponents for the complex SAR data to be processed.

The trim phase history Block 120 makes sure at a very high level thatthe two SAR images are suitable for interferometric processing. Theintersection of the respective phase histories in frequency space isselected between the two SAR images, and everything else is discarded.The two SAR images are registered in Block 122. Registration makes surethat features between the two SAR images are aligned. For example, acorner of a building at a given latitude/longitude/height in the firstSAR image is registered to correspond to a samelatitude/longitude/height in the second SAR image. As a result, thepixels are lined up between the two SAR images.

The interferogram is formed in Block 124. The first SAR image ismultiplied by the complex conjugate of the second SAR image. The resultis a difference in phase between the two SAR images. The resultinginterferogram is directly related to height. In the interferogram, thephase for each pixel is obtained by taking the arctan of its imaginarypart divided by its real part. For the first SAR image, the phase datafor each pixel is determined. Likewise, the phase data for each pixel isdetermined for the second SAR image. As will be discussed in detailbelow, the phase data for each pixel varies of the interferogram betweenminus pi and plus pi. Consequently, the phases wrap around.

Next, a low pass filter would normally be applied to smooth theinterferogram. However, this has a tendency to blur the edges in thescene. For a rural scene blurring is acceptable, but for an urban scenein which subsidence is being measured at specific landmarks, blurring isnot desirable since this effects the accuracy of the measuredsubsidence.

In lieu of a low pass filter, a complex anisotropic diffusion algorithmas discussed above is applied in Block 126 and a shock filter is appliedin Block 128. With the complex anisotropic diffusion algorithm, the realand imaginary parts of each pixel are processed as a complex object,i.e., non-linear processing. In contrast, linear processing involvesseparately processing the real and imaginary parts and then combiningthe results together. The complex anisotropic diffusion algorithmlocally mitigates noise while at the same time preserving scenediscontinuities in the interferogram.

The shock filter is used for image deblurring as readily understood bythose skilled in the art. In other words, the boundaries in the sceneare enhanced using mathematical morphology. The equation correspondingto the shock filter is as follows:

$\begin{matrix}{\frac{\partial{u\left( {x,y,t} \right)}}{\partial t} = {{- {{sign}\left( {\nabla^{2}{u\left( {x,y,t} \right)}} \right)}}{{\nabla{u\left( {x,y,t} \right)}}}}} & (4)\end{matrix}$

The shock equation is a non-linear hyperbolic differential equation. Thefirst part of the equation corresponds to the erosion/dilation that isdetermined by the Laplacian. The second part of the equation is amagnitude of the gradient. To illustrate application of the shockfilter, an original close vector multi-spectral image is shown in FIG.16, and application of the shock filter to the image is shown in FIG.17. The boundaries are noticeable sharper after application of the shockfilter.

The non-linear smoothing via the complex anisotropic algorithm and theboundary enhancement via the shock filter increases the accuracy andquality of the phase difference measurement. Since subsidence ismeasured using persistent objects in the scene (i.e., buildings) asreference points, improving boundary quality of the persistent objectsimproves the subsidence measurement.

Since the phase can only vary between plus pi to minus pi, it is calleda wrapped phase. If there is no ambiguity wrap in the phases between thetwo SAR images, the subsidence can then be measured in Block 130.However, if an ambiguity wrap does exist, as is typically the case, thena variational phase unwrap is performed in Block 132.

The variational phase unwrap is applied to the interferogram, which isthe phase difference between the registered first and second SAR images.The phase difference between the two registered SAR images is related toa desired physical quantity of interest, such as surface topography. Thephase difference can be registered only modulo 2π, and currentinterferometric techniques mainly recover the absolute phase (theunwrapped phase) from the registered one (wrapped phase) using discretevalues. Current phase unwrapping may be performed by residue-cut treealgorithms and least-square algorithms, for example.

To perform phase unwrapping, the phase is determined from theinterferogram, which is a complex object with real and imagery parts.The arctan of the imaginary part over the real part provides therespective phases. The amplitude is discarded and the phase is left.

Since the phase can only vary between plus pi to minus pi, it is calleda wrapped phase. In reality, however, the phase goes from plus infinityto minus infinity. This is where the difficulties lie in theinterferometric process.

The goal is to determine the proper mapping to go from plus/minus pispace to plus/minus infinity space. However, the finite images arelimited by the height of the tallest object in the scene. If the tallestbuilding is 800 feet, then the difference is based on the level atground and 800 feet. In theory, plus/minus infinity is mathematicallycorrect, but realistically the variation is between zero and the heightof the tallest object in the scene.

The variational phase unwrap deals with non-linearities anddiscontinuities in the data. Ambiguity exists at the phase wraps at theplus/minus pi boundaries before taking the phase difference between thetwo SAR images. The point at which the phase wraps is known as thefringe lines.

The variational phase unwrapping algorithm is two-dimensional.One-dimensional phase unwrapping techniques can be re-derived fortwo-dimensions using requirements that apply specifically to thesubsidence problem. Other two-dimensional phase unwrapping techniquesthat are available can also be tailored.

In image analysis, segmentation is the partitioning of a digital imageinto multiple regions (sets of pixels) according to some criterion. Thegoal of segmentation is typically to locate objects of interest. Somecommon techniques for segmentation include thresholding, region-growingand connect-component labeling. Active contours is also a common method.

The variational phase unwrapping algorithm is based on the Mumford-Shahfunction or cost function, as provided below:

$\begin{matrix}{{{E\left( {f,\overset{\rightarrow}{C}} \right)} = {{\beta{\int_{\Omega}{\left( {f - g} \right)^{2}\ {\mathbb{d}A}}}} + {\alpha{\int_{\Omega \smallsetminus \overset{\rightarrow}{C}}{{{\nabla f}}^{2}\ {\mathbb{d}A}}}} + {\gamma{\oint_{\overset{\rightarrow}{C}}\ {\mathbb{d}s}}}}}{\beta{\int_{\Omega}{\left( {f - g} \right)^{2}\ {\mathbb{d}A}}}}{\int_{\Omega\backslash\overset{\rightarrow}{C}}{{{\nabla f}}^{2}\ {\mathbb{d}A}}}{\gamma{\oint_{\overset{\rightarrow}{C}}\ {\mathbb{d}s}}}} & (5)\end{matrix}$

The equation determines what f and C will provide the unwrapped phase.The first term is the f piece-wise smooth approximation to g (the image)with discontinuities along C. This part of the equation may be thoughtof as a data fidelity term measuring the quality of f. The second termof the equation is the smoothness term. This may be viewed as the priormodel for f given C. The third term corresponds to the length of C.Normally there is a penalty for excessive arc length. The originalMumford-Shah function or cost function used the Hausdorff measure formore general sets of discontinuities. In accordance with the presentinvention, C is restricted to be a smooth curve in order to be replacedby the arc length.

To minimize the Mumford-Shah function or cost function, a new costfunction is developed to better address the discontinuity of databetween the fringe lines. The new cost function is as follows:

$\begin{matrix}\begin{matrix}{{E\left( {f,\overset{\rightarrow}{C}} \right)} = {{\beta{\int_{\Omega}{\left( {f_{x} - g_{x}} \right)^{2}\ {\mathbb{d}A}}}} + {\beta{\int_{\Omega}{\left( {f_{y} - g_{y}} \right)^{2}\ {\mathbb{d}A}}}} +}} \\{{\alpha{\int_{\Omega \smallsetminus \overset{\rightarrow}{C}}{{{\nabla f}}^{2}\ {\mathbb{d}A}}}} + {\gamma{\oint_{\overset{\rightarrow}{C}}\ {\mathbb{d}s}}}}\end{matrix} & (6)\end{matrix}$

The first term expresses the gradients between the wrapped and unwrappedphase. The second term expresses prior knowledge of the scene to beprocessed. The third term imposes limits on the maximum fringe length ofthe unprocessed interferogram. Most phase unwrapping algorithms work onsmooth data. After determining the minimal solution for the costfunction E(f,C), a conversion is made to a partial differential equation(PDE). The PDE is then solved.

The variational phase unwrapping algorithm takes advantage of the factthat the data is preprocessed with the complex anisotropic diffusionalgorithm. The complex anisotropic diffusion algorithm is designed notto smooth discontinuities. Consequently, the variational approach to thephase unwrapping takes advantage that the data will still bediscontinuous.

Variational phase unwrapping will now be discussed in reference to theplots shown in FIGS. 18-27. An original scene of two buildings 150, 152and the corresponding ground 160 adjacent to the buildings is providedin FIGS. 18A and 18B. FIG. 18B is a three-dimensional plot of the SARimage, and FIG. 18A is a top down view of the same SAR image. In theoriginal scene, the ground 160 is a hill that is nearly as tall as oneof the buildings 152.

A wrapped interferogram of the original scene is provided in FIGS. 19Aand 19B. Since the interferogram is a complex objects its phase isdetermined so that the interferogram can be viewed. Consequently, theaxis of the plot in FIG. 19B is in radians. For each x and y pixel thereis a phase value, which is wrapped. This means that the range is alwaysbetween plus/minus pi. The edges of the plus/minus pi range are thefringe lines 170 and 172.

In FIG. 19B, the center of the hill 160 has dropped. The phasedifference does not include any noise. Noise is artificially added tothe scene to simulate a real collection, as shown in FIGS. 20A and 20B.In FIGS. 21A and 21B, the noise has been mitigated with the applicationof a smoothing filter. Since normal smoothing algorithms have a tendencyto smooth discontinuities, the fringe lines 170, 172 have been smoothed.As a result, the resolution has been reduced. A complex anisotropicdiffuser interferogram is provided in FIGS. 22A and 22B. The fringelines 170, 172 are sharper, and the building edges are preserved.

Another set of examples will now be discussed in reference to FIGS.23-27. A top down view of four buildings 180, 182, 184 and 186 is shownin FIG. 23. Each building is at a different height as indicated by adifferent shade. A noiseless interferogram is shown in FIG. 24. Becauseof the phase wrapping between plus/minus pi, the four buildings 180,182, 184 and 186 look to be the same height as indicated by the sameshade. When noise is added to the interferogram, the buildings 180, 182,184 and 186 become hidden by the noise, as shown in FIG. 25. A complexanisotropically diffused noisy interferogram in accordance with theinvention is shown in FIG. 26. The buildings 180, 182, 184 and 186 areextracted from the noise after the application of the complexanisotropic diffusion algorithm. A close-up view of building 180 isprovided in FIG. 27 to illustrate how the boundary edges are maintained.

Referring back to the flowchart in FIG. 15, the geometry of each SARproviding a respective SAR image is estimated in Block 134. Adetermination is made as to where each SAR was located at the time thecorresponding image was taken. If the first SAR was pointing at a givenlatitude/longitude/height, then there will be a high confidence in thepixel values as far as what the latitude/longitude/height is for thatpixel.

The unwrapped phase in radians is converted to height in Block 136. Whenan unwrapped phase measurement is obtained it is in radians. Aconversion is then made from radians to height. A closed form equationtakes the radian value to height as readily understood by those skilledin the art. The height provides the necessary measurement to determinesubsidence between the two SAR images. For illustration purposes, anoriginal image is shown in FIG. 28, the original image smoothed with aGaussian filter is shown in FIG. 29, and the original image filteredwith a complex anisotropic diffusion algorithm after 20 iterations isshown in FIG. 30. Between the two images, the boundary edges arenoticeably crisper in FIG. 30.

The discontinuity is thus maintained by the anisotropic diffusionalgorithm because of the properties of the algorithm. The algorithmbasically operates on the gradients so it knows based strictly on thegradient of the image whether or not to smooth. If the gradient is belowthe noise threshold that is set up front, the algorithm is going to goahead and smooth the gradient. If the gradient is above the threshold,the algorithm will not smooth the gradient in order to maintain orpreserve an edge of a corresponding structure. As noted above, thethreshold is preferably set based on knowledge of the scene.

The variational phase unwrapping is derived from the error mathematicscalled variational calculus. The principles of variational calculus areused to come up with a phase unwrapping algorithm that deals withdiscontinuities. Current unwrapping algorithms have a tendency to smooththe data. In contrast, the variational phase unwrapping algorithm isbased on a cost function for controlling the smoothing. Instead ofproviding a global smoothing based on the properties of the data, thevariational phase unwrapping algorithm leaves edges intact andselectively smoothes the area adjacent the edges.

Another aspect of the invention is application of the anisotropicdiffusion algorithm when compressing and decompressing SAR images. SARdata sets can be relatively large, and when a SAR data set iscompressed, the resolution is usually lowered during the process. Thisis a result of lossy preprocessing compression schemes. Lossypreprocessing algorithms usually degrade the scatterers in a scene.Moreover, the volume of data can overwhelm current processingcapabilities.

Most common preprocessing algorithms act as low-pass filters. Thefollowing compression schemes attempt to group the data in a way thatfinds similarities throughout the data: independent component analysis(ICA), wavelet transform (Gabor filters) and parallelism exploitationschemes. Due to the dynamic range of the SAR data, it is difficult tothreshold the data in a way such that the data can be grouped well.

A computer-implemented method for compressing SAR images comprisesreceiving a SAR image to be compressed, applying an anisotropicdiffusion algorithm to the SAR image, and compressing the SAR imageafter applying the anisotropic diffusion algorithm thereto. An advantageof applying the anisotropic diffusion algorithm is that the size of theSAR image after compression results in a smaller size file, regardlessof the compression scheme used. Consequently, storage and transmissionof the compressed SAR image occupies less space and bandwidth.

For comparison purposes, the metrics for compression are based on theoriginal scene shown in FIG. 31. The original scene has an uncompressedTiff file size of 691 kB. JPEG compression of the original scene reducesthe file size to 62 kB, whereas Winzip compression of the original scenereduces the file size to 41 kB.

Filtering of the original scene with a Gaussian filter is shown in FIG.32. JPEG compression of the Gaussian filtered original image reduces thefile size to 55 kB, whereas Winzip compresses of the Gaussian filteredoriginal image reduces the file size to 33 kB.

In accordance with the present invention, filtering of the originalscene an anisotropic diffusion algorithm is shown in FIG. 33. The sizeof the anisotropic diffused filtered scene is still the same size as theoriginal scene without filtering and with Gaussian filtering. JPEGcompression of the anisotropicly diffused scene reduces the file size to44 kB, whereas Winzip compression of the anisotropicly diffused scenereduces the file size to 23 kB.

TABLE 1 provides a side-by-side comparison between the different images.When anisotropic diffusion has been applied to any of the SAR images,greater compression can be achieved than when the anisotropic diffusionalgorithm was not applied. The anisotropic diffusion filtered image hasa JPESG compression of 16:1 and a Winzip compression ratio of 30:1.

TABLE 1 Compression Ratio Table Image Uncompressed JPEG Type TiffCompression Winzip Original 1 11:1 16:1 Image Gaussian 1 13:1 21:1Filtered Anisotropic 1 16:1 30:1 Filtered

In terms of decompression, decompression is performed based onanisotropic diffusion. More particularly, a computer-implemented methodfor decompressing SAR images comprises receiving a SAR image to bedecompressed, performing a dynamic range compression on the SAR image,quantizing the compressed SAR image, and decompressing the quantizedcompressed SAR image by applying an anistropic diffusion algorithmthereto. The quantization may be in unit8, for example. The dynamicrange compression is a non-linear process.

Reference is now directed to FIGS. 34-36 to illustrate thedecompression. The original image to be decompressed is shown in FIG.34, and has an image storage size of 65.68 MB. Non-linear dynamic rangecompression is applied and the results are quantized in unit8, as shownin FIG. 35. The image storage size is now 4.11 MB. Tree and shadows arenot well defined in the quantized image.

Decompression of the quantized image with an anisotropic diffusionalgorithm is shown in FIG. 36. Trees and shadows are now better defined.A shock filter may even be applied to further enhance the viewed SARimage. By dynamically compressing the SAR data and then quantizing thatdata, the amount of data that is required during transmission issignificantly reduced. On average, these data sets would require4.11/65.7=6.25% of the data of the scene for transmission. The greaterthe dynamic range the greater the compression ratio. For very brightscatterers in a scene, it gets compressed even more. Even if a user isprovided with a lossy compressed/decompressed image, application of theabove compression/decompression approaches will actually improve thequality of the original image for viewing.

Elevation extraction/registration using anisotropic diffusion asdiscussed above for noisy imagery and SAR imagery will now be discussedin reference to FIGS. 37-44. Noisy data effects the accuracy ofcorrelation, registration (same or cross sensor) and elevationextraction. Currently, low pass filters are used for noisy data.Apodization is used for SAR data.

A computer-implemented method for registering SAR images comprisesselecting first and second SAR images to be registered, individuallyprocessing the selected first and second SAR images with an anisotropicdiffusion algorithm, and registering the first and second SAR imagesafter the processing. A shock filter is preferably applied to therespective first and second processed SAR images before the registering.Elevation data may then be extracted based on the registered SAR images.

For illustration purposes, two unregistered SAR images are shown inFIGS. 37 and 38. To obtain metrics on the advantages of using ananisotropic diffusion algorithm during the registration, correlation isbetween image 1 in FIG. 37 which is the reference, and image 2 in FIG.38 which is the sub-image. A correlation coefficient map for the twoimages is determined. The maximum value of the correlation map isobtained, i.e., the peak. The peak location on the correlation mapindicates the shift between the data, i.e., the registered shift. All ofimage 1 is correlated with all of image 2. The maximum value of thecorrelation is obtained after registration. The image is then filteredusing the anisotropic diffusion algorithm.

The correlation peak is 0.9081 in the unfiltered correlation surface asshown in FIG. 39. After filtering, the correlation peak is 0.9674 asshown in FIG. 40. After registration is applied, the images from FIGS.37 and 38 do not move, as shown in FIGS. 41 and 42. These images are notfiltered. The correlation peak for registration is 0.9081 and the postcorrelation of the entire image after registration is 0.3634. Afterfiltering, the corresponding images are shown in FIG. 43-44. Thecorrelation peak for registration is 0.9674 and the post correlation ofthe entire image after registration is 0.8094. Registration is improvedby 9.4%, and correlation is improved after registration by 45%.

Vector and road extraction using non-linear anisotropic diffusionprocessing and shock filters for material classification will now bediscussed in reference to FIGS. 45-48. There is a demand for roadextraction of single reflective scenes. Currently, smoothing kernels areapplied to the data. Morphological filters are also applied(dilation/erosion) to the data. Vector/road extraction may then beprovided using a Gaussian/zero crossing filter.

In accordance with the present invention, a computer-implemented methodfor vector extraction in SAR images comprises selecting a SAR image forvector extraction, processing the selected SAR image with an anisotropicdiffusion algorithm, and extracting vector data based on the processedSAR image. A shock filter may be applied to the processed SAR imagebefore the extracting. Road image data may then be extracted based onthe extracted vector data.

The data is thus delineated using a coherent scheme of the anisotropicdiffusion algorithm. This scheme provides higher accuracy for roadextraction. An original scene is shown in FIG. 45. After anisotropicdiffusion is applied, the target is better defined, as shown in FIG. 46.In general, targets are better delineated for single reflectivesurfaces. This lends itself well for segmentation.

The anisotropic diffusion algorithm may also be applied to other imagesafter they have already been filtered by other filter types. Thesefilter types include a Prewitt filter and a Roberts filter, for example.An original image that was filtered by a Prewitt filter is shown in FIG.47. FIG. 48 shows the same image after application of the anisotropicdiffusion algorithm. The illustrated target as well as the chain linkfence are better defined.

In addition, other features relating to SAR images are disclosed incopending patent applications filed concurrently herewith and assignedto the assignee of the present invention and are entitled METHOD ANDAPPARATUS FOR PROCESSING SAR IMAGES BASED ON AN ANISOTROPIC DIFFUSIONFILTERING ALGORITHM, Ser. No. 11/689,591; METHOD AND APPARATUS FORPROCESSING SAR IMAGES BASED ON A COMPLEX ANISOTROPIC DIFFUSION FILTERINGALGORITHM, Ser. No. 11/689,616; METHOD AND APPARATUS FOR REGISTRATIONAND VECTOR EXTRACTION OF SAR IMAGES BASED ON AN ANISOTROPIC DIFFUSIONFILTERING ALGORITHM, Ser. No. 11/689,727; and METHOD AND APPARATUS FORPROCESSING COMPLEX INTERFEROMETRIC SAR DATA, Ser. No. 11/689,743, theentire disclosures of which are incorporated herein in their entirety byreference.

Many modifications and other embodiments of the invention will come tothe mind of one skilled in the art having the benefit of the teachingspresented in the foregoing descriptions and the associated drawings.Therefore, it is understood that the invention is not to be limited tothe specific embodiments disclosed, and that modifications andembodiments are intended to be included within the scope of the appendedclaims.

1. A computer-implemented method for decompressing synthetic apertureradar (SAR) images comprising: receiving a SAR image; performing adynamic range compression on the SAR image; quantizing the compressedSAR image; and decompressing the quantized compressed SAR image byapplying an anistropic diffusion algorithm thereto.
 2. Acomputer-implemented method according to claim 1 wherein the dynamicrange compression is a non-linear process.
 3. A computer-implementedmethod according to claim 1 wherein applying the anisotropic diffusionalgorithm comprises: determining noise in the quantized compressed SARimage; selecting a noise threshold for the quantized compressed SARimage based on the determined noise; and mathematically adjusting theanisotropic diffusion algorithm based on the selected noise threshold.4. A computer-implemented method according to claim 3 wherein theanisotropic diffusion algorithm is based on a heat equation comprising anon-constant term; and wherein mathematically adjusting the anisotropicdiffusion algorithm comprises adjusting the non-constant term.
 5. Acomputer-implemented method according to claim 4 wherein thenon-constant term in the heat equation comprises a convection function.6. A computer-implemented method according to claim 3 further comprisingdetermining gradient values of the quantized compressed SAR image; andwherein determining the noise is based on statistical analysis of thegradient values.
 7. A computer-implemented method according to claim 6wherein the statistical analysis is based on a standard deviation of thegradient values plus a constant.
 8. A computer-implemented methodaccording to claim 3 wherein determining the noise is based on a Fourierwindowing scheme.
 9. A computer-implemented method according to claim 3wherein determining the noise is based on a wavelet decomposition.
 10. Acomputer-implemented method according to claim 1 further comprisingdisplaying the decompressed SAR image.
 11. A computer-implemented methodaccording to claim 1 wherein the anisotropic diffusion algorithm isbased on a second order linear partial differential equation.
 12. Acomputer-implemented method according to claim 1 wherein the SAR imagecomprises complex data.
 13. A computer-implemented method fordecompressing synthetic aperture radar (SAR) images comprising:receiving a SAR image; performing a dynamic range compression on the SARimage; quantizing the compressed SAR image; and determining gradientvalues of the quantized compressed SAR image; selecting a noisethreshold for the quantized compressed SAR image based on a statisticalanalysis of the gradient values; providing an anisotropic diffusionalgorithm based on a heat equation comprising a convection function;mathematically adjusting the convection function based on the selectednoise threshold; and decompressing the SAR image by applying theadjusted anisotropic diffusion algorithm thereto.
 14. Acomputer-implemented method according to claim 13 wherein thestatistical analysis is based on a standard deviation of the gradientvalues plus a constant.
 15. A computer-implemented method according toclaim 13 wherein the anisotropic diffusion algorithm is based on asecond order linear partial differential equation.
 16. Acomputer-implemented method according to claim 13 further comprisingdisplaying the decompressed SAR image.
 17. A computer-implemented methodaccording to claim 13 wherein the SAR image comprises complex data. 18.A computer-readable medium having computer-executable instructions forcausing a computer to perform steps comprising: receiving a SAR image;performing a dynamic range compression on the SAR image; quantizing thecompressed SAR image; and decompressing the quantized compressed SARimage by applying an anistropic diffusion algorithm thereto.
 19. Acomputer-readable medium according to claim 18 wherein the dynamic rangecompression is a non-linear process.
 20. A computer-readable mediumaccording to claim 18 wherein applying the anisotropic diffusionalgorithm comprises: determining noise in the quantized compressed SARimage; selecting a noise threshold for the quantized compressed SARimage based on the determined noise; and mathematically adjusting theanisotropic diffusion algorithm based on the selected noise threshold.21. A computer-readable medium according to claim 20 wherein theanisotropic diffusion algorithm is based on a heat equation comprising anon-constant term; and wherein mathematically adjusting the anisotropicdiffusion algorithm comprises adjusting the non-constant term.
 22. Acomputer-readable medium according to claim 21 wherein the non-constantterm in the heat equation comprises a convection function.
 23. Acomputer-readable medium according to claim 20 further comprisingdetermining gradient values of the quantized compressed SAR image; andwherein determining the noise is based on statistical analysis of thegradient values.
 24. A computer-readable medium according to claim 23wherein the statistical analysis is based on a standard deviation of thegradient values plus a constant.
 25. A computer-readable mediumaccording to claim 20 wherein determining the noise is based on aFourier windowing scheme.
 26. A computer-readable medium according toclaim 20 wherein determining the noise is based on a waveletdecomposition.
 27. A computer-readable medium according to claim 18further comprising displaying the decompressed SAR image.
 28. Acomputer-readable medium according to claim 18 wherein the anisotropicdiffusion algorithm is based on a second order linear partialdifferential equation.
 29. A computer-readable medium according to claim18 wherein the SAR image comprises complex data.
 30. A computer systemfor decompressing synthetic aperture radar (SAR) images comprising: adatabase for storing SAR images; and a processor for decompressing a SARimage from said database, the decompressing comprising receiving the SARimage, performing a dynamic range compression on the SAR image,quantizing the compressed SAR image, and decompressing the quantizedcompressed SAR image by applying an anistropic diffusion algorithmthereto.
 31. A computer system according to claim 30 wherein the dynamicrange compression is a non-linear process.
 32. A computer systemaccording to claim 30 wherein applying the anisotropic diffusionalgorithm comprises: determining noise in the quantized compressed SARimage; selecting a noise threshold for the quantized compressed SARimage based on the determined noise; and mathematically adjusting theanisotropic diffusion algorithm based on the selected noise threshold.33. A computer system according to claim 32 wherein the anisotropicdiffusion algorithm is based on a heat equation comprising anon-constant term; and wherein mathematically adjusting the anisotropicdiffusion algorithm comprises adjusting the non-constant term.
 34. Acomputer system according to claim 33 wherein the non-constant term inthe heat equation comprises a convection function.
 35. A computer systemaccording to claim 32 wherein applying the anisotropic diffusionalgorithm further comprises determining gradient values of the quantizedcompressed SAR image; and wherein determining the noise is based onstatistical analysis of the gradient values.
 36. A computer systemaccording to claim 35 wherein the statistical analysis is based on astandard deviation of the gradient values plus a constant.
 37. Acomputer system according to claim 30 further comprising a displaycoupled to said processor for displaying the decompressed SAR image. 38.A computer system according to claim 30 wherein the SAR image comprisescomplex data.